Accuracy Assessment of Coastal Topography Derived From Uav Images

Unmanned aerial vehicles (UAVs) are cost-effective remote sensing tools useful for generating very high-resolution (VHR) aerial imagery. Habitat maps generated from UAV imagery are a fundamental component of marine spatial planning, essential for the designation and governance of marine protected areas (MPAs). We investigated whether UAV survey altitude affects habitat classification performance and the classification accuracy of thematic maps from a tropical shallow water environment. We conducted repeated UAV flights at 75, 85, and 110 m, using a fixed-wing UAV on the Turneffe Atoll, Belize. Flights were ground truthed with snorkel surveys. Images were mosaiced to form orthomosaics and transformed into thematic maps through semi-automatic object-based image analysis (OBIA). Three subset areas (4000 m 2 , 17 000 m 2 and 17 000 m 2) from two cayes on the atoll were selected to investigate the effect of survey altitude. A linear regression demonstrated that for every 1 m increase in survey altitude, there was a~1% decrease in the overall classification accuracy. A low survey altitude of 75 m produced a higher classification accuracy for thematic maps and increased the representation of mangrove, seagrass and sand. The variability in classified cover was driven by altitude, although the direction and extent of this relationship was specific to each class. For coral and sea, classified cover decreased with increased altitude. Mangrove classified cover was non-sensitive to altitude changes, demonstrating a lesser need for a consistent survey altitude. Sand and seagrass had a greater sensitivity to altitude, due to classified cover variability between altitudes. Our findings suggest that survey altitude should be minimized when classifying tropical marine environments (coral, seagrass) and, given that most fixed-wing UAVs are restricted to a minimum altitude of 70 m, we recommend an altitude of 75 m. Survey altitude should be a major consideration when targeting habitats with greater sensitivity to altitude variability.

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“…2012; Long et al. 2016), and producing a high accuracy (>70%) in object‐based classification (Rau et al. 2012).…”

Section: Introductionmentioning

confidence: 99%

Influence of altitude on tropical marine habitat classification using imagery from fixed‐wing, water‐landing UAVs

Ellis

Taylor

Schiele

et al. 2020

Remote Sens Ecol Conserv

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“…We demonstrated that computing resources, software, data, and algorithms are fully capable of analyzing a large volume of data at multiple spatial scales to derive an understanding of the complex changes and the interplay among the various coastal processes at work in this area. Future research could build upon this multi-scale approach by using imagery obtained from unmanned aerial systems (UASs) at a very local scale where land cover and shoreline changes have been identified [41][42][43][44][45]. Instrumentation aboard unmanned aerial vehicles (UAVs) can provide excellent spatial resolution as well as multispectral bands; however, the coverage is limited because of battery time for overflights and access to launch and retrieve equipment can be problematic [44,45].…”

Section: Discussionmentioning

confidence: 99%

A Methodology to Assess Land Use Development, Flooding, and Wetland Change as Indicators of Coastal Vulnerability

Halls

Magolan

2019

Remote Sensing

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Coastal areas around the world are becoming increasingly urban, which has increased stress to both natural and anthropogenic systems. In the United States, 52% of the population lives along the coast, and North Carolina is in the top 10 fastest growing states. Within North Carolina, the southeastern coast is the fastest growing region in the state. Therefore, this research has developed a methodology that investigates the complex relationship between urbanization, land cover change, and potential flood risk and tested the approach in a rapidly urbanizing region. A variety of data, including satellite (PlanetScope) and airborne imagery (NAIP and Lidar) and vector data (C-CAP, FEMA floodplains, and building permits), were used to assess changes through space and time. The techniques consisted of (1) matrix change analysis, (2) a new approach to analyzing shorelines by computing adjacency statistics for changes in wetland and urban development, and (3) calculating risk using a fishnet, or tessellation, where hexagons of equal size (15 ha) were ranked into high, medium, and low risk and comparing these results with the amount of urbanization. As other research has shown, there was a significant relationship between residential development and wetland loss. Where urban development has yet to occur, most of the remaining area is at risk to flooding. Importantly, the combined methods used in this study have identified at-risk areas and places where wetlands have migrated/transgressed in relationship to urban development. The combination of techniques developed here has resulted in data that local government planners are using to evaluate current development regulations and incorporating into the new long-range plan for the County that will include smart growth and identification of risk. Additionally, results from this study area are being utilized in an application to the Federal Emergency Management Agency’s Community Response System which will provide residents with lower flood insurance costs.

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“…Future work could test the classification of barrier island geomorphology using unmanned aerial vehicle (UAV) imagery; however, a digital terrain model (DTM) is not equivalent to a DEM and therefore fieldwork should be conducted to correctly calibrate UAV imagery [37][38][39]. The areas mapped in this study have plenty of Lidar data; however, this is not always the case and so UAV imagery may be a useful alternative.…”

Section: Discussionmentioning

confidence: 99%

An Automated Model to Classify Barrier Island Geomorphology Using Lidar Data and Change Analysis (1998–2014)

2018

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Abstract:Previous research has documented the usefulness of Lidar data to derive a variety of topographic products (e.g., DEM, DTM, canopy and forest structure, and urban infrastructure). Lidar has been used to map coastal environments and geomorphology; however, there is no comprehensive model to derive coastal geomorphology. Therefore, the purpose of this project was to build on existing research and develop an automated modeling approach to classify coastal geomorphology across barrier islands. The model was developed and tested at four sites in North Carolina including two undeveloped and two developed islands. Barrier island geomorphology is shaped by natural coastal processes, such as storms and longshore sediment transport, as well as human influences, such as beach nourishment and urban development. The model was developed to classify ten geomorphic features over four time-steps from 1998 to 2014. Model results were compared to compute change through time and derived the rate and direction of feature movement. Tropical storms and hurricanes had the most influence in geomorphic change and movement. On the developed islands, there was less influence of storms due to the inability of features to move because of coastal infrastructure. From 2005 to 2010, beach nourishment was the dominant influence on developed beaches because this activity ameliorated the natural tendency for an island to erode. Understanding how natural and anthropogenic processes influence barrier island geomorphology is critical to predicting an island's future response to changing environmental factors such as sea-level rise. The development of an automated model enables it to be replicated in other locations where policy makers and coastal managers may use this information to make development and conservation decisions.

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Accuracy Assessment of Coastal Topography Derived From Uav Images

Cited by 15 publications

References 8 publications

Influence of altitude on tropical marine habitat classification using imagery from fixed‐wing, water‐landing UAVs

Influence of altitude on tropical marine habitat classification using imagery from fixed‐wing, water‐landing UAVs

A Methodology to Assess Land Use Development, Flooding, and Wetland Change as Indicators of Coastal Vulnerability

An Automated Model to Classify Barrier Island Geomorphology Using Lidar Data and Change Analysis (1998–2014)

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